RoHNAS: A Neural Architecture Search Framework With Conjoint Optimization for Adversarial Robustness and Hardware Efficiency of Convolutional and Capsule Networks

RoHNAS: A Neural Architecture Search Framework With Conjoint Optimization for Adversarial Robustness and Hardware Efficiency of Convolutional and Capsule Networks

Neural Architecture Search (NAS) algorithms aim at finding efficient Deep Neural Network (DNN) architectures for a given application under given system constraints. DNNs are computationally-complex as well as vulnerable to adversarial attacks. In order to address multiple design objectives, we propo...

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Bibliographic Details
Main Authors: Alberto Marchisio, Vojtech Mrazek, Andrea Massa, Beatrice Bussolino, Maurizio Martina, Muhammad Shafique
Format: Article
Language:English
Published: IEEE 2022-01-01
Series:IEEE Access
Subjects:
Adversarial robustness
energy efficiency
latency
memory
hardware-aware neural architecture search
evolutionary algorithm
Online Access:https://ieeexplore.ieee.org/document/9917535/

Internet

https://ieeexplore.ieee.org/document/9917535/

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